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'''Dynamic causal modeling''' (DCM) is a [[Bayes factor|Bayesian model comparison]] procedure for comparing models of how data were generated. Dynamic causal models are formulated in terms of [[Stochastic differential equation|stochastic]] or [[Ordinary differential equation|ordinary differential equations]] (i.e., nonlinear [[State space|state-space]] models in continuous time). These equations model the dynamics of [[Hidden Markov model|hidden states]] in the nodes of a [[Graphical model|probabilistic graphical model]], where conditional dependencies are parameterized in terms of directed [[Brain connectivity estimators|effective connectivity]].
DCM was initially developed for identifying models of [[Dynamical system|neural dynamics]], estimating their parameters and comparing their evidence.<ref name=":2">{{Cite journal|last=Friston|first=K.J.|last2=Harrison|first2=L.|last3=Penny|first3=W.|date=2003-08|title=Dynamic causal modelling|url=https://doi.org/10.1016/S1053-8119(03)00202-7|journal=NeuroImage|volume=19|issue=4|pages=1273–1302|doi=10.1016/s1053-8119(03)00202-7|issn=1053-8119}}</ref>. DCM allows one to test competing models of interactions among neural populations (effective connectivity) using functional neuroimaging data e.g., [[functional magnetic resonance imaging]] (fMRI), [[magnetoencephalography]] (MEG) or [[electroencephalography]] (EEG).
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